Litellm staging (#8270)

* fix(opik.py): cleanup

* docs(opik_integration.md): cleanup opik integration docs

* fix(redact_messages.py): fix redact messages check header logic

ensures stringified bool value in header is still asserted to true

 allows dynamic message redaction

* feat(redact_messages.py): support `x-litellm-enable-message-redaction` request header

allows dynamic message redaction

docs/my-website/docs/observability/opik_integration.md

@@ -1,3 +1,5 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 import Image from '@theme/IdealImage';
 
 # Comet Opik - Logging + Evals
@@ -21,17 +23,16 @@ Use just 4 lines of code, to instantly log your responses **across all providers
 Get your Opik API Key by signing up [here](https://www.comet.com/signup?utm_source=litelllm&utm_medium=docs&utm_content=api_key_cell)!
 
 ```python
-from litellm.integrations.opik.opik import OpikLogger
 import litellm
-
-opik_logger = OpikLogger()
-litellm.callbacks = [opik_logger]
+litellm.callbacks = ["opik"]
 ```
 
 Full examples:
 
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
 ```python
-from litellm.integrations.opik.opik import OpikLogger
 import litellm
 import os
 
@@ -43,8 +44,7 @@ os.environ["OPIK_WORKSPACE"] = ""
 os.environ["OPENAI_API_KEY"] = ""
 
 # set "opik" as a callback, litellm will send the data to an Opik server (such as comet.com)
-opik_logger = OpikLogger()
-litellm.callbacks = [opik_logger]
+litellm.callbacks = ["opik"]
 
 # openai call
 response = litellm.completion(
@@ -55,18 +55,16 @@ response = litellm.completion(
 )
 ```
 
-If you are liteLLM within a function tracked using Opik's `@track` decorator,
+If you are using liteLLM within a function tracked using Opik's `@track` decorator,
 you will need provide the `current_span_data` field in the metadata attribute
 so that the LLM call is assigned to the correct trace:
 
 ```python
 from opik import track
 from opik.opik_context import get_current_span_data
-from litellm.integrations.opik.opik import OpikLogger
 import litellm
 
-opik_logger = OpikLogger()
-litellm.callbacks = [opik_logger]
+litellm.callbacks = ["opik"]
 
 @track()
 def streaming_function(input):
@@ -87,6 +85,126 @@ response = streaming_function("Why is tracking and evaluation of LLMs important?
 chunks = list(response)
 ```
 
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo-testing
+    litellm_params:
+      model: gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+litellm_settings:
+  callbacks: ["opik"]
+
+environment_variables:
+  OPIK_API_KEY: ""
+  OPIK_WORKSPACE: ""
+```
+
+2. Run proxy
+
+```bash
+litellm --config config.yaml
+```
+
+3. Test it! 
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+  "model": "gpt-3.5-turbo-testing",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What's the weather like in Boston today?"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Opik-Specific Parameters
+
+These can be passed inside metadata with the `opik` key.
+
+### Fields 
+
+- `project_name` - Name of the Opik project to send data to.
+- `current_span_data` - The current span data to be used for tracing.
+- `tags` - Tags to be used for tracing.
+
+### Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from opik import track
+from opik.opik_context import get_current_span_data
+import litellm
+
+litellm.callbacks = ["opik"]
+
+messages = [{"role": "user", "content": input}]
+response = litellm.completion(
+    model="gpt-3.5-turbo",
+    messages=messages,
+    metadata = {
+        "opik": {
+            "current_span_data": get_current_span_data(),
+            "tags": ["streaming-test"],
+        },
+    }
+)
+return response
+```
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+  "model": "gpt-3.5-turbo-testing",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What's the weather like in Boston today?"
+    }
+  ],
+  "metadata": {
+    "opik": {
+      "current_span_data": "...",
+      "tags": ["streaming-test"],
+    },
+  }
+}'
+``` 
+
+</TabItem>
+</Tabs>
+
+
+
+
+
+
+
+
+
+
+
+
+
 ## Support & Talk to Founders
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)

docs/my-website/docs/proxy/logging.md

@@ -1,3 +1,7 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Logging
 
 Log Proxy input, output, and exceptions using:
@@ -13,9 +17,7 @@ Log Proxy input, output, and exceptions using:
 - DynamoDB
 - etc.
 
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
+
 
 ## Getting the LiteLLM Call ID
 
@@ -77,10 +79,13 @@ litellm_settings:
 
 ### Redact Messages, Response Content
 
-Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata will still be logged.
+Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked.
 
+<Tabs>
 
-Example config.yaml
+<TabItem value="global" label="Global">
+
+**1. Setup config.yaml **
 ```yaml
 model_list:
  - model_name: gpt-3.5-turbo
@@ -91,9 +96,87 @@ litellm_settings:
   turn_off_message_logging: True # 👈 Key Change
 ```
 
-If you have this feature turned on, you can override it for specific requests by
+**2. Send request**
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+        {
+        "role": "user",
+        "content": "what llm are you"
+        }
+    ]
+}'
+```
+
+
+
+</TabItem>
+<TabItem value="dynamic" label="Per Request">
+
+:::info
+
+Dynamic request message redaction is in BETA. 
+
+:::
+
+Pass in a request header to enable message redaction for a request.
+
+```
+x-litellm-enable-message-redaction: true
+```
+
+Example config.yaml
+
+**1. Setup config.yaml **
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+```
+
+**2. Setup per request header**
+
+```shell
+curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-zV5HlSIm8ihj1F9C_ZbB1g' \
+-H 'x-litellm-enable-message-redaction: true' \
+-d '{
+  "model": "gpt-3.5-turbo-testing",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Hey, how'\''s it going 1234?"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+**3. Check Logging Tool + Spend Logs**
+
+**Logging Tool**
+
+<Image img={require('../../img/message_redaction_logging.png')}/>
+
+**Spend Logs**
+
+<Image img={require('../../img/message_redaction_spend_logs.png')} />
+
+
+### Disable Message Redaction
+
+If you have `litellm.turn_on_message_logging` turned on, you can override it for specific requests by
 setting a request header `LiteLLM-Disable-Message-Redaction: true`.
 
+
 ```shell
 curl --location 'http://0.0.0.0:4000/chat/completions' \
     --header 'Content-Type: application/json' \
@@ -109,8 +192,6 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 }'
 ```
 
-Removes any field with `user_api_key_*` from metadata.
-
 
 ### Turn off all tracking/logging
 

docs/my-website/docs/proxy/request_headers.md

@@ -6,6 +6,8 @@ Special headers that are supported by LiteLLM.
 
 `x-litellm-timeout` Optional[float]: The timeout for the request in seconds.
 
+`x-litellm-enable-message-redaction`: Optional[bool]: Don't log the message content to logging integrations. Just track spend. [Learn More](./logging#redact-messages-response-content)
+
 ## Anthropic Headers
 
 `anthropic-version` Optional[str]: The version of the Anthropic API to use.  

litellm/integrations/opik/opik.py

@@ -147,13 +147,11 @@ class OpikLogger(CustomBatchLogger):
                     f"OpikLogger - Error: {response.status_code} - {response.text}"
                 )
             else:
-                verbose_logger.debug(
+                verbose_logger.info(
                     f"OpikLogger - {len(self.log_queue)} Opik events submitted"
                 )
         except Exception as e:
-            verbose_logger.exception(
-                f"OpikLogger failed to send batch - {str(e)}\n{traceback.format_exc()}"
-            )
+            verbose_logger.exception(f"OpikLogger failed to send batch - {str(e)}")
 
     def _create_opik_headers(self):
         headers = {}
@@ -165,7 +163,7 @@ class OpikLogger(CustomBatchLogger):
         return headers
 
     async def async_send_batch(self):
-        verbose_logger.exception("Calling async_send_batch")
+        verbose_logger.info("Calling async_send_batch")
         if not self.log_queue:
             return
 
@@ -177,10 +175,12 @@ class OpikLogger(CustomBatchLogger):
             await self._submit_batch(
                 url=self.trace_url, headers=self.headers, batch={"traces": traces}
             )
+            verbose_logger.info(f"Sent {len(traces)} traces")
         if len(spans) > 0:
             await self._submit_batch(
                 url=self.span_url, headers=self.headers, batch={"spans": spans}
             )
+            verbose_logger.info(f"Sent {len(spans)} spans")
 
     def _create_opik_payload(  # noqa: PLR0915
         self, kwargs, response_obj, start_time, end_time

litellm/litellm_core_utils/redact_messages.py

@@ -73,12 +73,9 @@ def perform_redaction(model_call_details: dict, result):
         return {"text": "redacted-by-litellm"}
 
 
-def redact_message_input_output_from_logging(
-    model_call_details: dict, result, input: Optional[Any] = None
-):
+def should_redact_message_logging(model_call_details: dict) -> bool:
     """
-    Removes messages, prompts, input, response from logging. This modifies the data in-place
-    only redacts when litellm.turn_off_message_logging == True
+    Determine if message logging should be redacted.
     """
     _request_headers = (
         model_call_details.get("litellm_params", {}).get("metadata", {}) or {}
@@ -86,25 +83,48 @@ def redact_message_input_output_from_logging(
 
     request_headers = _request_headers.get("headers", {})
 
+    possible_request_headers = [
+        "litellm-enable-message-redaction",  # old header. maintain backwards compatibility
+        "x-litellm-enable-message-redaction",  # new header
+    ]
+
+    is_redaction_enabled_via_header = False
+    for header in possible_request_headers:
+        if bool(request_headers.get(header, False)):
+            is_redaction_enabled_via_header = True
+            break
+
     # check if user opted out of logging message/response to callbacks
     if (
         litellm.turn_off_message_logging is not True
-        and request_headers.get("litellm-enable-message-redaction", False) is not True
+        and is_redaction_enabled_via_header is not True
         and _get_turn_off_message_logging_from_dynamic_params(model_call_details)
         is not True
     ):
-        return result
+        return False
 
-    if request_headers and request_headers.get(
-        "litellm-disable-message-redaction", False
+    if request_headers and bool(
+        request_headers.get("litellm-disable-message-redaction", False)
     ):
-        return result
+        return False
 
     # user has OPTED OUT of message redaction
     if _get_turn_off_message_logging_from_dynamic_params(model_call_details) is False:
-        return result
+        return False
 
+    return True
+
+
+def redact_message_input_output_from_logging(
+    model_call_details: dict, result, input: Optional[Any] = None
+) -> Any:
+    """
+    Removes messages, prompts, input, response from logging. This modifies the data in-place
+    only redacts when litellm.turn_off_message_logging == True
+    """
+    if should_redact_message_logging(model_call_details):
         return perform_redaction(model_call_details, result)
+    return result
 
 
 def _get_turn_off_message_logging_from_dynamic_params(

litellm/model_prices_and_context_window_backup.json

@@ -8904,16 +8904,5 @@
         "supports_function_calling": true,
         "mode": "chat",
         "supports_tool_choice": true
-    },
-    "hyperbolic/deepseek-v3": {
-        "max_tokens": 20480,
-        "max_input_tokens": 131072,
-        "max_output_tokens": 20480,
-        "litellm_provider": "openai",
-        "input_cost_per_token": 0.00000025,
-        "output_cost_per_token": 0.00000025,
-        "mode": "chat",
-        "supports_function_calling": true,
-        "supports_response_schema": true
     }
 }

litellm/proxy/_new_secret_config.yaml

@@ -29,21 +29,4 @@ model_list:
       
 
 litellm_settings:
-  callbacks: ["langsmith"]
-  disable_no_log_param: true
-
-general_settings:
-  enable_jwt_auth: True
-  litellm_jwtauth:
-    object_id_jwt_field: "client_id" # can be either user / team, inferred from the role mapping
-    roles_jwt_field: "resource_access.litellm-test-client-id.roles"
-    role_mappings:
-      - role: litellm.api.consumer
-        internal_role: "team"
-    enforce_rbac: true
-  role_permissions: # default model + endpoint permissions for a role. 
-    - role: team
-      models: ["anthropic-claude"]
-      routes: ["openai_routes"]
-
-
+  callbacks: ["opik"]